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Walgreen s approach caught on with his people, who in turn captured his customers From 1920 to 1925, the chain expanded from 20 to 65 stores, all but six of them in Chicago Such rapid growth, coupled with Walgreens noticeably consistent, principled approach to customer service, naturally started drawing attention from professional observers of such things The University of Chicago (U-C) found itself surrounded by Walgreens South Side stores by middecade, so it was only a matter of time before someone there decided to take a closer look In 1925, a business professor named James O McKinsey, who later served as chairman of Marshall Field and Company, became increasingly curious about the well-run company and suggested that one of his prot g s, Robert Knight, make a formal study of the chain for his master s thesis What started as a casual office-hour chat became a report that would help shape the company far beyond World War II Robert Greenwell Knight was born in England and raised in Canada, where he starred in the classroom and on the debate team two skills that would serve him well throughout his life at the University of Manitoba in Winnipeg He migrated to Chicago to study for his master s degree at U-C s Department of Commerce and Administration (now called the Graduate School of Business), where he met Professor McKinsey Once McKinsey unleashed Knight on the Walgreens assignment, the young graduate sunk his teeth into it like it was red meat, spending most of 1925 dissecting everything from individual window displays to corporate

gets large In some applications the transition probabilities pij have the property that for each state i the probability pij = 0 for j i 2 (or pij = 0 for j i + 2) Then the linear equations are of the Hessenberg type Linear equations of the Hessenberg type can be ef ciently solved by a special code using the very stable QR method In solving the Markov chain equations (341) and (342) by a direct method, one of the equilibrium equations is omitted to obtain a square system of linear equations Iterative method of successive overrelaxation Iterative methods have to be used when the size of the system of linear equations gets large In speci c applications an iterative method can usually avoid computer memory problems by exploiting the (sparse) structure of the application An iterative method does not update the matrix of coef cients each time In applications these coef cients are usually composed from a few constants Then only these constants have to be stored in memory when using an iterative method In addition to the advantage that the coef cient matrix need not be stored, an iterative method is easy to program for speci c applications The iterative method of successive overrelaxation is a suitable method for solving the linear equations of large Markov chains The well-known Gauss Seidel method is a special case of the method of successive overrelaxation The iterative methods generate a sequence of vectors x(0) x(1) x(2) converging towards a solution of the equilibrium equations (341) The normalization is done at the end of the calculations To apply successive overrelaxation, we rst rewrite the equilibrium equations (341) in the form

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aij xj ,

MCC = mobile country code MNC = mobile network code LAC = location area code RAC = routing area code

i = 1, , N,

The standard successive overrelaxation method uses a xed relaxation factor for speeding up the convergence The method starts with an initial approximation vector x(0) = 0 In the kth iteration of the algorithm an approximation vector x(k) is (k) found by a recursive computation of the components xi such that the calculation (k) (k) of the new estimate xi uses both the new estimates xj for j < i and the old estimates xj(k 1) for j > i The steps of the algorithm are as follows: Step 0 Choose a non-zero vector x(0) Let k := 1 Step 1 Calculate successively for i = 1, , N the component xi(k) from

109

j =1

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